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ArticleFlexible shape memory structures with low activation temperatures through investigation of the plasticizing effect(IOP Publishing Ltd, 2025-05-28)Shape memory polymer (SMP) systems exhibiting semicrystalline- elastomer blends, such as thermoplastic polyurethane and polylactic acid have been well studied, but their use in biomedical shape memory applications has been limited by their high activation temperature. SMPs are capable of deformation and recovery through the activation of an external stimuli, such as temperature. Critical criteria for SMPs used in biomedical applications is achieving a stimulus temperature close to 37 °C while still experiencing sufficient shape recovery. A polymer's glass transition temperature has been well defined as the SMP system's activation temperature and therefore should be decreased to achieve a decreased activation temperature. In this work, a well-known, biocompatible plasticizer, polyethylene oxide (PEO), was added to thermoplastic polyurethane (TPU)—polylactic acid (PLA) SMP blends to observe the plasticizing effect on the structural, thermal, mechanical, and shape memory properties of the polymer blends. Additionally, the geometry of the fabricated SMP samples was tailored to further enhance the shape memory effect through a bowtie honeycomb structure. Our results suggest that the addition of PEO into the SMP system may be an effective method for decreasing the polymer's glass transition temperature through the alteration of the polymer chain structure. With the addition of 30% PEO, the glass transition temperature of the TPU/PLA blend was successfully decreased from 62.4 °C to 34.6 °C while achieving 86.5% shape recovery when activated at 37 °C, which is only a 5% decrease in shape recovery when activated at 50 °C. These results suggest that the addition of a biocompatible plasticizer may overcome the limitation of employing temperature activated SMP systems in biomedical applications, and enhances the potential of these materials for reconfigurable structures, energy dissipation systems, and structural health monitoring (SHM) in civil engineering applications.
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ArticleA field calibration method that improves soil heat flux accuracy(Wiley Periodicals LLC on behalf of Soil Science Society of America, 2025-05-19)Soil heat flux plates (SHFPs) are widely used to measure soil heat flux (Gs). Gs is often underestimated by SHFPs (Gp). Although calibration methods are used, they are not always effective. The objective of this study is to evaluate the effectiveness of a field calibration method applied to various SHFPs installed in a full canopy maize field. A 5-day measurement period with wet and dry soil conditions was used for calibration, while 80-day and 60-day measurement periods were used for evaluation. Uncorrected SHFP measured values (Gp) underestimated the actual reference Gs determined by the gradient method (Gs_grad) by 42%–64%. Gp values in the evaluation period were corrected (Gp_corr) by dividing them by the ratio of Gp/Gs_grad determined over the calibration period. After the correction, the Gp_corr agreed well with the Gs_grad, with Gp_corr/Gs_grad of four of six SHFPs being 0.90–1.01, improving to 74%–98%. The field calibration performed approximately the same with the wet and dry calibration periods, whether the calibration and evaluation periods were consecutive in time or had relatively long time intervals, indicating that this method accounted for almost all errors with SHFP. This is largely due to the slight variation in soil thermal conductivity and the linearity between soil temperature gradients from SHFP and the gradient method under relatively stable soil moisture conditions. This study deepens our understanding and improves the accuracy of soil heat flux measurements. Calibration of SHFPs under various land covers and weather conditions is warranted in future studies.
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PreprintLogarithmic resilience risk metrics that address the huge variations in blackout cost( 2025-05-17)Resilience risk metrics must address the customer cost of the largest blackouts of greatest impact. However, there are huge variations in blackout cost in observed distribution utility data that make it impractical to properly estimate the mean large blackout cost and the corresponding risk. These problems are caused by the heavy tail observed in the distribution of customer costs. To solve these problems, we propose resilience metrics that describe large blackout risk using the mean of the logarithm of the cost of large-cost blackouts, the slope index of the heavy tail, and the frequency of large-cost blackouts.
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ArticleLaser-induced graphene with nickel oxide nanoparticles electrochemical immunosensor for rapid and label-free detection of Salmonella enterica Typhimurium(Springer, 2025-05-17)Cost-effectiveness, high-throughput capability, and scalable manufacturing are key features required for the fabrication of in-field electrochemical sensors applicable to food safety analysis. In this work, a two-step method is described to create laser-induced graphene electrodes conjugated with nickel oxide nanoparticles (LIG-NiO). Fabrication of the LIG-NiO electrodes is 2 performed via direct writing under ambient conditions using polyimide sheet and nickel acetate solution as substrates, which is then converted into a label-free immunosensor for the detection of Salmonella enterica serovar Typhimurium by functionalizing the working surface with an anti-Salmonella antibody. The resulting electrochemical immunosensor exhibits a sensitivity of 3.93 ± 0.25 Ω (log (CFU mL-1)-1) limit of detection (LOD) of 8 ± 3 CFU mL-1 and rapid response time (17 min) with a wide Salmonella Typhimurium linear sensing range, from 101 to 106 CFU mL-1 in buffer, covering relevant levels for food safety analysis without being affected by the presence of interferent bacteria Escherichia coli spp. Additionally, this LIG-NiO-based immunosensor presented a sensitivity of 1.92 ± 0.71 Ω (log (CFU mL-1)-1) when tested in chicken broth. The immunosensor developed in this study provides a simple fabrication method followed by functionalization and rapid Salmonella Typhimurium sensing that does not require sample pretreatment such as pre-enrichment or addition of external reagents, constituting a promising new sensing platform for pathogen detection in food safety monitoring and in general to other electrochemical immunosensing applications.
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ArticleArtificial intelligence in the design, optimization, and performance prediction of concrete materials: a comprehensive review(Nature Research, 2025-05-17)Artificial Intelligence (AI) is transforming concrete research. This review explores various AI techniques that drive cutting-edge solutions across all stages of concrete lifecycle, from material, mixture, and process optimization to quality control and performance prediction. Meta-analysis shows that XGBoost model excels in predicting workability (R2 = 0.98), while ensemble models provide the best strength predictions (R2 = 0.93). The study highlights trends, gaps, and future AI opportunities in concrete technology.